1. Field of the Invention
The present invention relates generally to the art of passive preference detection, and more particularly to a system, method, and apparatus for automatically determining the media content preferences of a user who downloads streaming media content via the Internet.
2. Description of the Related Art
There are music-distribution systems in the art that record the music preferences of the users of such systems, and play back songs based on those preferences. There are also Internet sites, for example, that allow users to manually assign a score to songs, where the score reflects the user's enjoyment of the song. Based on the user's scores, such sites intelligently select songs to send to the user that the user is likely to enjoy.
Such systems have major drawbacks, however, because a score must be manually entered for each song. Entering scores is very cumbersome and may be very confusing for new or unsophisticated users. Moreover, in a portable environment, such as in a car or on a portable player, such an elaborate controller may be difficult and/or costly to implement.
Also, such systems only use each particular user's scores when calculating which songs to send to that particular user. A drawback of this approach is that such a system may only select songs that user will like with any degree of accuracy after that user has already entered scores for a large number of songs.
Accordingly, a preference detection system is desired that does not require a user to manually score songs. A system capable of passively determining a user's music preferences is therefore desired. Such a system should be capable of learning a user's preferences based on the user's responses (such as forwarding to the next song, etc.) while each song plays. Such a system should work not only with music, but also with other types of media (video, etc.).
A preference detection system is also desired that learns which songs to send a user not only based upon that user's responses, but also based upon the responses of other users to similar songs, as patterns may appear when data from enough users is analyzed.